{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "_RNN_Many_to_One_TF2.0.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.1"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qlseAUvJJDXM"
      },
      "source": [
        "## The Data\n",
        "\n",
        "**Source: https://datamarket.com/data/set/22ox/monthly-milk-production-pounds-per-cow-jan-62-dec-75#!ds=22ox&display=line**\n",
        "\n",
        "**Monthly milk production: pounds per cow. Jan 62 - Dec 75**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5WTUlbh4JDXN"
      },
      "source": [
        "Import numpy pandas and matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Xg2peEzGJDXO",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hBtEtgN6JDXS"
      },
      "source": [
        "**Use pandas to read the csv of the monthly-milk-production.csv file and set index_col='Month'**\n",
        "\n",
        "Mounting google drive to read the csv file. \n",
        "\n",
        "Alternatively, you can download the csv file to your local disk drive "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FPJdTW0TJcMA",
        "outputId": "4a30ac3d-418d-4b9e-f601-c302edfc882b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nArYXHMPJDXT",
        "colab": {}
      },
      "source": [
        "milk = pd.read_csv(\"gdrive/My Drive/dataML/monthly-milk-production.csv\",index_col='Month')\n",
        "\n",
        "#milk = pd.read_csv(\"monthly-milk-production.csv\",index_col='Month')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RYvb00fEJDXW",
        "outputId": "4dcf764a-f5e7-401e-b1b0-6306f09d6ecc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "milk.tail()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Milk Production</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1975-08-01 01:00:00</th>\n",
              "      <td>858.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-09-01 01:00:00</th>\n",
              "      <td>817.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-10-01 01:00:00</th>\n",
              "      <td>827.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-11-01 01:00:00</th>\n",
              "      <td>797.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-12-01 01:00:00</th>\n",
              "      <td>843.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Milk Production\n",
              "Month                               \n",
              "1975-08-01 01:00:00            858.0\n",
              "1975-09-01 01:00:00            817.0\n",
              "1975-10-01 01:00:00            827.0\n",
              "1975-11-01 01:00:00            797.0\n",
              "1975-12-01 01:00:00            843.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q7lczwPW3JoP",
        "colab_type": "code",
        "outputId": "9ba35684-73ee-472a-9302-7b2f68c75f15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "type(milk.index[0])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Z0aPxAkWJDXg"
      },
      "source": [
        "**Make the index a time series by using:**\n",
        "\n",
        "    milk.index = pd.to_datetime(milk.index)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ej2PnSz7JDXh",
        "colab": {}
      },
      "source": [
        "milk.index = pd.to_datetime(milk.index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "5c122230-10bd-48e3-e088-864d767bcfc5",
        "id": "ueYCkL_O9GFc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "type(milk.index[12])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas._libs.tslibs.timestamps.Timestamp"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TuNcJhMoJDXj",
        "outputId": "37111070-d2de-43c7-9ec6-3d2b5ad9d1c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "milk.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Milk Production</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1962-01-01 01:00:00</th>\n",
              "      <td>589.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-02-01 01:00:00</th>\n",
              "      <td>561.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-03-01 01:00:00</th>\n",
              "      <td>640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-04-01 01:00:00</th>\n",
              "      <td>656.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-05-01 01:00:00</th>\n",
              "      <td>727.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Milk Production\n",
              "Month                               \n",
              "1962-01-01 01:00:00            589.0\n",
              "1962-02-01 01:00:00            561.0\n",
              "1962-03-01 01:00:00            640.0\n",
              "1962-04-01 01:00:00            656.0\n",
              "1962-05-01 01:00:00            727.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-rgkKTs6JDXn",
        "outputId": "025127e2-0484-4b12-cab4-429a0096e673",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "milk.keys()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Milk Production'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "z8Hk38WeJDXq"
      },
      "source": [
        "** Plot out the time series data. **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qXNiSsZUJDXq",
        "outputId": "6ea5d29a-8bba-478e-fca8-adbda16538d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "milk.plot()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fde2089bdd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2trBmTlRH7gV1tg/QugUB89t/ddcAAMi2o5r1+7wOuf3E3deNo1ASMBvNYCmR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\n8VZYrWT/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Fxbb9fhP1rsU"
      },
      "source": [
        "### Train Test Split\n",
        "\n",
        "** Let's attempt to predict a year's worth of data. (12 months or 12 steps into the future) **\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TSYpC-nu1rsW",
        "outputId": "8311aaee-bf79-49d6-b67e-3b00b983d588",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "milk.info()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 168 entries, 1962-01-01 01:00:00 to 1975-12-01 01:00:00\n",
            "Data columns (total 1 columns):\n",
            "Milk Production    168 non-null float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 2.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "00vc2Jms1rsc",
        "outputId": "e572bdff-e8a9-4d70-ed04-5373dd1ab55a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(milk)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "168"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "F_PjB7rq1rsf",
        "colab": {}
      },
      "source": [
        "train_set= milk.head((1976-1962-1)*12)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "C6632CCp1rsi",
        "colab": {}
      },
      "source": [
        "test_set = milk.tail(12)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_9PtX6Um1rsl"
      },
      "source": [
        "### Scale the Data\n",
        "\n",
        "** Use sklearn.preprocessing to scale the data using the MinMaxScaler. Remember to only fit_transform on the training data, then transform the test data. You shouldn't fit on the test data as well, otherwise you are assuming you would know about future behavior!**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "czuAVyRj1rsl",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UFn7xXzj1rsp",
        "colab": {}
      },
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hu0-OYDX1rsq",
        "colab": {}
      },
      "source": [
        "train_scaled = scaler.fit_transform(train_set)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "O2uDgWsI1rsu",
        "colab": {}
      },
      "source": [
        "test_scaled = scaler.transform(test_set)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EiI-RuSPKC7v"
      },
      "source": [
        "## Build Training Data\n",
        "\n",
        "* X_train: Past 12 monthes productions, shape: (-1,12)\n",
        "* Y_train: Future 1 month productions, shape:(-1, 1) \n",
        "* Shift the window to get more trainning data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6YaF3wjKKB4Y",
        "colab": {}
      },
      "source": [
        "def build_train_data(data, past_monthes = 12, future_monthes = 1):\n",
        "  X_train, Y_train = [],[]\n",
        "  \n",
        "  for i in range(data.shape[0] + 1 - past_monthes - future_monthes):\n",
        "    X_train.append(np.array(data[i:i + past_monthes]))\n",
        "    Y_train.append(np.array(data[i + past_monthes:i + past_monthes + future_monthes]))\n",
        "    \n",
        "  return np.array(X_train).reshape([-1,12]), np.array(Y_train).reshape([-1,1])\n",
        "   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "t_z8xNdaQjvH",
        "colab": {}
      },
      "source": [
        "x, y = build_train_data(train_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DiLmBOy4Qq3j",
        "outputId": "4d842e55-44e5-4c26-8ed6-835df2b1dd82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print('x shape;', x.shape, 'y shape: ', y.shape)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x shape; (144, 12) y shape:  (144, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JxbPdOLds9JD"
      },
      "source": [
        "Take a look at the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3_tRprRSjkYY",
        "outputId": "5938a9b1-4f9a-4b8f-988b-1b2955df4a28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.plot(x[12])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fde11a46940>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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b7e15iu+475IkzkvozCMLN7P3UJVDnrMtmpoMy5p3V9RxReVJokIDeHZKGjvt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\nXSmlvMT/B4SMr4Bhf01sAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PWw-Y-h72UGT",
        "colab_type": "text"
      },
      "source": [
        "## Tensorflow 2.0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_OPQ7tg2YkD",
        "colab_type": "code",
        "outputId": "3e860458-67b8-4a25-dd31-9ef72a6753f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVJ9OEiX2k5n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1c6Ugs3e-Mjd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "RNN_CELLSIZE = 10\n",
        "SEQLEN = 12\n",
        "BATCHSIZE = 1\n",
        "LR = 0.001"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hn7eqj712yyg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class GRUModel(tf.keras.Model):\n",
        "  def __init__(self, batch_size, seq_length, cell_size):\n",
        "    super().__init__()\n",
        "    self.batch_size = batch_size\n",
        "    self.seq_length = seq_length\n",
        "    self.cell_size = cell_size\n",
        "    \n",
        "    self.layer1 = tf.keras.layers.Reshape((self.seq_length,1), batch_size = self.batch_size)\n",
        "    self.layer_GRU = tf.keras.layers.GRU(self.cell_size, return_sequences=True)\n",
        "    self.layer_last_GRU = tf.keras.layers.GRU(self.cell_size)\n",
        "    self.layer_dense = tf.keras.layers.Dense(1)\n",
        "  \n",
        "  def call(self, inputs):\n",
        "    x = self.layer1(inputs)\n",
        "    x = self.layer_GRU(x)\n",
        "    x = self.layer_last_GRU(x)\n",
        "    output = self.layer_dense(x)\n",
        "    return output"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZYeFiWM-ON9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = GRUModel(BATCHSIZE, SEQLEN,RNN_CELLSIZE)\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate = LR)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUXBn9aN_DOb",
        "colab_type": "code",
        "outputId": "f18490a4-51e0-43d3-8a0c-e5f031feee35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        }
      },
      "source": [
        "for epoch in range(1000):\n",
        "  with tf.GradientTape() as tape:\n",
        "    y_pred = model(x)\n",
        "    loss = tf.reduce_mean((y_pred - y)**2)\n",
        "    if epoch%100 == 0:\n",
        "      print(\"epoch: {}, loss: {}\".format(epoch, loss.numpy()))\n",
        "  \n",
        "  grads = tape.gradient(loss, model.variables)\n",
        "  optimizer.apply_gradients(zip(grads,model.variables))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 0, loss: 0.0028520694468170404\n",
            "epoch: 100, loss: 0.002558263950049877\n",
            "epoch: 200, loss: 0.002333666430786252\n",
            "epoch: 300, loss: 0.0022316505201160908\n",
            "epoch: 400, loss: 0.0020229490473866463\n",
            "epoch: 500, loss: 0.001882405485957861\n",
            "epoch: 600, loss: 0.0017968474421650171\n",
            "epoch: 700, loss: 0.0018584158970043063\n",
            "epoch: 800, loss: 0.0016114676836878061\n",
            "epoch: 900, loss: 0.0015710649313405156\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "r714-jBM1Yo-"
      },
      "source": [
        "### Predict Future \n",
        "* Use the last 12 monthes data of the train data set to predict the future 12 monthes production \n",
        "* Compare the predicted data with the real data in test data set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QkYcxMMgqGN0"
      },
      "source": [
        "Get the last 12 monthes data of the train data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wN_zImU5_T-7",
        "colab": {}
      },
      "source": [
        "train_seed = list(train_scaled[-12:].flatten())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lJXmc5C_qPi4"
      },
      "source": [
        "One prediction only generate one predict, we need predict 12 times to get 12 monthes data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gPLrd2196eHx",
        "colab": {}
      },
      "source": [
        "def get_prediction(data_list):\n",
        "  predict = []\n",
        "  train_seed = data_list\n",
        "  for i in range(12):\n",
        "    x_train = np.array(train_seed[-12:]).reshape(1,12)\n",
        "    one_predict = model.predict(x_train)[0][0]\n",
        "    predict.append(one_predict) \n",
        "    train_seed.append(one_predict)\n",
        "   \n",
        "  return predict, train_seed"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BT-RPo6k9XrQ",
        "colab": {}
      },
      "source": [
        "predict, train_seed = get_prediction(train_seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "f_p2HvS2Ce12",
        "outputId": "8717553c-fe60-4fd2-b3ad-276ade780832",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.plot(train_seed)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fdd7a851da0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Tz+iY5vnjdqNDESHw6inP4Duj57dM59aqXKxmk1wcDTBJ6EFSZ+slIc5EqQEjSyeqKUyj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\npWuVkAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "H5VAR8RQ98LN",
        "colab": {}
      },
      "source": [
        "results = scaler.inverse_transform(np.array(predict).reshape(12,1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bJgzXnlWDMp3",
        "outputId": "35034798-7a37-488e-b86d-09441b9f2de4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "test_set['Generated'] = results"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "omtVz1Crqo9f"
      },
      "source": [
        "Compare the generated data with the real data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bPZAXKyMDVAD",
        "outputId": "1403ae5f-b10e-4a8a-a658-b1767991868e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "source": [
        "test_set"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Milk Production</th>\n",
              "      <th>Generated</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1975-01-01 01:00:00</th>\n",
              "      <td>834.0</td>\n",
              "      <td>827.510132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-02-01 01:00:00</th>\n",
              "      <td>782.0</td>\n",
              "      <td>806.098389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-03-01 01:00:00</th>\n",
              "      <td>892.0</td>\n",
              "      <td>879.101196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-04-01 01:00:00</th>\n",
              "      <td>903.0</td>\n",
              "      <td>904.815430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-05-01 01:00:00</th>\n",
              "      <td>966.0</td>\n",
              "      <td>953.602356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-06-01 01:00:00</th>\n",
              "      <td>937.0</td>\n",
              "      <td>950.758484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-07-01 01:00:00</th>\n",
              "      <td>896.0</td>\n",
              "      <td>926.439514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-08-01 01:00:00</th>\n",
              "      <td>858.0</td>\n",
              "      <td>888.573364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-09-01 01:00:00</th>\n",
              "      <td>817.0</td>\n",
              "      <td>837.547363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-10-01 01:00:00</th>\n",
              "      <td>827.0</td>\n",
              "      <td>819.590332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-11-01 01:00:00</th>\n",
              "      <td>797.0</td>\n",
              "      <td>786.962280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-12-01 01:00:00</th>\n",
              "      <td>843.0</td>\n",
              "      <td>809.943604</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Milk Production   Generated\n",
              "Month                                           \n",
              "1975-01-01 01:00:00            834.0  827.510132\n",
              "1975-02-01 01:00:00            782.0  806.098389\n",
              "1975-03-01 01:00:00            892.0  879.101196\n",
              "1975-04-01 01:00:00            903.0  904.815430\n",
              "1975-05-01 01:00:00            966.0  953.602356\n",
              "1975-06-01 01:00:00            937.0  950.758484\n",
              "1975-07-01 01:00:00            896.0  926.439514\n",
              "1975-08-01 01:00:00            858.0  888.573364\n",
              "1975-09-01 01:00:00            817.0  837.547363\n",
              "1975-10-01 01:00:00            827.0  819.590332\n",
              "1975-11-01 01:00:00            797.0  786.962280\n",
              "1975-12-01 01:00:00            843.0  809.943604"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dsUl0PbuQ-QF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8b9610c9-5f00-41c6-a42a-b76bd2a4c0e6"
      },
      "source": [
        "type(test_set.index[2])"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas._libs.tslibs.timestamps.Timestamp"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6v-Ot8ZxDZvi",
        "outputId": "4d1740df-82ec-4c28-b72b-4db73bc61239",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "test_set.plot()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd7a91f630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wuVx7SveaxZSSKqGb0kueFTh4MZKZO0No4FySF+qU0fcCRUpqSyR/fwEW9YSh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\nXQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "WkrgRIqzkXgq"
      },
      "source": [
        "### Predict more \n",
        "Use the first months data to predict the future 13 years' milk productions and compare with the real data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sbRNA4ERklYA",
        "colab": {}
      },
      "source": [
        "train_seed = list(train_scaled[:12].flatten())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "F91e0IzvklYE",
        "colab": {}
      },
      "source": [
        "def get_prediction(data_list):\n",
        "  predict = []\n",
        "  train_seed = data_list\n",
        "  for i in range(12*13):\n",
        "    x_train = np.array(train_seed[-12:]).reshape(1,12)\n",
        "    one_predict = model.predict(x_train)[0][0]\n",
        "    predict.append(one_predict) \n",
        "    train_seed.append(one_predict)\n",
        "   \n",
        "  return predict, train_seed"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BNW_8HJ2klYG",
        "colab": {}
      },
      "source": [
        "predict, train_seed = get_prediction(train_seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TvgurkwHklYI",
        "outputId": "800e81f0-5865-4dd8-8c15-5bd3e71407dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.plot(train_seed)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fdd7a73b198>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mm9u2QEgX69tsFQRWa9oFGYmD0ETYj81KQ/pGlS7VkIwGpconZc8HtLMGCZkI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\ndQHYGtzuFkMV8Dnn+NzZBUt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sBHxxzmBWNy4yYbPSzBupEtrcmm97J+DrV+q8urSJRNgv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\nEMQ+gQI+QRDEPuH/Bw7CGAMfgNxGAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pcNqRCbiklYN",
        "colab": {}
      },
      "source": [
        "results = scaler.inverse_transform(np.array(train_seed).reshape(-1,1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "K-8x-sOFmBav",
        "colab": {}
      },
      "source": [
        "milk_predict = milk"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "387zn4SBmKsl",
        "colab": {}
      },
      "source": [
        "milk_predict['Generated'] = results"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PDvQXKtjklYX",
        "outputId": "20c97e28-2f88-409e-b3b2-7e4b92ffcb19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "milk_predict.plot()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd7a73b5c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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psMjdgoW+8KOXlvnVf36M52d7by3bDn5lhaRN9BfZv5CD2Na+PfdsqcJzM2l2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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zIAwIfd6JDa8",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}